Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.
Abstract
Knowledge models in radiotherapy capture the relation between patient anatomy and
dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing
models struggle to predict accurately. We propose a case-based reasoning framework
designed to handle novel anatomies that are of same type but vary beyond original
training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were
analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node
cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario,
and Ample scenario. For the Scarce scenario, a multiple stepwise regression model
was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed
workflow started with evaluating the feature novelty of new cases against 5 training
prostate-plus-lymph-node cases using leverage statistic. The case database was composed
of a 5-case dose atlas. Case-based dose prediction was compared against the regression
model prediction using sum of squared residual. Mean sum of squared residual of case-based
and regression predictions for the bladder of 13 identified outliers were 0.174 ±
0.166 and 0.459 ± 0.508, respectively (P = .0326). For the rectum, the respective
mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based
and regression prediction (P = .1972). By retaining novel cases, under the Ample scenario,
significant statistical improvement was observed over the Scarce scenario (P = .0398)
for the bladder model. We expect that the incorporation of case-based reasoning that
judiciously applies appropriate predictive models could improve overall prediction
accuracy and robustness in clinical practice.
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https://hdl.handle.net/10161/19363Published Version (Please cite this version)
10.1177/1533033819874788Publication Info
Sheng, Yang; Zhang, Jiahan; Wang, Chunhao; Yin, Fang-Fang; Wu, Q Jackie; & Ge, Yaorong (2019). Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic
Case Study. Technology in cancer research & treatment, 18. pp. 1533033819874788. 10.1177/1533033819874788. Retrieved from https://hdl.handle.net/10161/19363.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Chunhao Wang
Assistant Professor of Radiation Oncology
Deep learning methods for image-based radiotherapy outcome prediction and assessment
Machine learning in outcome modelling
Automation in radiotherapy planning and delivery
Qingrong Wu
Professor of Radiation Oncology
Fang-Fang Yin
Professor in Radiation Oncology
Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning
optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy,
image-guided radiation therapy, oncological imaging and informatics
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